Data Engineer

Join an amazing team that is consistently recognized for our achievements and culture, including our most recent Forbes award of being one of America's Best Midsize Employers for 2026!

Position Summary:

The Data Engineer I role focuses on enhancing automation framework features, developing tools, and integrating automation into the software development lifecycle. This position also involves working closely with cross-functional teams to minimize manual efforts and ensure that testing meets both business objectives and regulatory requirements.

Geo-Salary Information:

An in-person interview may be required during the hiring process

State specific pay scales for this role are as follows:
$83,670 to $161,815 (NJ, NY, WA, HI, AK, MD, CT, RI, MA)
$76,064 to $147,104 (NV, OR, AZ, CO, WY, TX, ND, MN, MO, IL, WI, FL, GA, MI, OH, VA, PA, DE, VT, NH, ME)
$68,457 to $132,394 (UT, , MT, NM, SD, NE, KS, OK, IA, AR, LA, MS, AL, TN, KY, IN, SC, NC, WV)

In CA:
Typical hiring range is $110,468 to $153,428

The expected base salary for this position will vary depending on a number of factors, including relevant experience, skills and location.

Essential Job Functions:

• Design, build, and launch collections of high-quality big data/data lake solutions on Cloud platform preferably AWS, that support multiple use cases across all departments, all products, and all states.
• Solve our most challenging data integration problems, utilizing optimal ETL patterns, frameworks, query techniques, sourcing from structured and unstructured data sources.
• Assist in owning existing processes running in production, optimizing complex code through advanced algorithmic concepts.
• The Data Engineer is an expert in all data lakes, data warehouses, and data cubes within Mercury, with no gaps in knowledge. Can efficiently and accurately extract and manipulate data from any source.
• Collaborate with teams of data analysts and data scientists, who research and integrate algorithms to develop solutions to address complex data problems. Influence all functions across the organization to identify data opportunities to drive profitable growth. Proactively identify pain points that Analytics & Data Science face with our existing data models.
• Leverages existing data infrastructure to fulfill all data-related requests, perform necessary data housekeeping, data cleansing, normalization, hashing, and implementation of required data model changes. Analyzes data to spot anomalies, trends and correlate similar data sets. Designs, develops and implements natural language processing software modules.
• Other functions may be assigned

Education:

Minimum:

• Bachelor's degree in Computer Engineering, Computer Science, Mathematics, Electrical Engineering, Information Systems, or related field OR equivalent combination of education and/or experience

Experience:

Minimum:

• 1 or more years of experience in data analytics, data engineering, and/or data science
• 1 or more years of experience in architecting/designing and leading development of big data/data lake solutions on Cloud platforms, preferably AWS (S3, Glue/EMR, Athena, App Flow)
• 1 or more years of experience in Python or Java programming
• 1 or more years of experience in writing SQL statements and query performance tuning
• 1 or more years of experience in RDMS or MPP databases, preferably AWS Redshift or Snowflake

Preferred:

• Experience supporting or building cloud-based data platform workflows in AWS, including services such as S3, Lambda, Docker, and EKS.
• Experience working with Spark for large-scale data processing, transformation, or dataset preparation within a data lake or big data platform environment.
• Exposure to data ingestion or integration pipelines, including working with internal and external application data, vendor API calls, and enabling secure data delivery into a data lake environment.
• Familiarity with CI/CD practices and cross-functional infrastructure support, including partnering with teams such as Infrastructure, DBA, and platform administration to provision and maintain access, environments, and orchestration tooling.
• Exposure to AI and automation.

Knowledge and

Skills:

Minimum:

• A high-level specialist who regularly interacts and works with senior management.
• Expert at analyzing data to identify gaps and inconsistencies
• Able to multitask, prioritize, and manage time effectively.
• The ability to think conceptually, analytically and creatively comfortable with ambiguity.
• Experience managing and communicating data plans…